Building distributed systems means accepting that events won't always arrive in the order they were created. When Services communicate asynchronously through message queues and event streams, race conditions become inevitable. This guide exploires practical strategies for handling out-of-order events using Go.
Real implementations over mocks, test fixtures that work, and design patterns that make code testable without generating mock files for every interface.
How we reduced GC pauses from 50ms to 2ms by finding hidden allocations. Practical pprof techniques, escape analysis, and the optimizations that actually matter.
Practical patterns for controlling concurrency: worker pools, errgroup, semaphores, fan-out/fan-in, and backpressure. How to process work concurrently without overwhelming your system.
Handling in-flight requests, Kafka consumers, database connections, and Kubernetes termination signals correctly. The details that tutorials skip.
Structured errors, error wrapping done right, sentinel errors vs behavior checking, and patterns that make debugging production issues actually possible.
Manual DI, functional options, Wire code generation, and why Go doesn't need Spring-style containers. Practical patterns for building testable, maintainable applications.
Real latency cascades we encountered, how they took down our system, and the circuit breaker implementation that saved us.
After years building distributed systems, here's my honest take on when you should consider the complexity, and why Go excels when you do.
When to pass context, when not to, and the common mistakes that lead to leaked goroutines, missed cancellations, and debugging nightmares.
Practical module boundaries, package organization, and the patterns that make future decomposition possible without rewriting everything.
A practical guide to implementing Clean Architecture in Golang with real examples and best practices for building modular, testable backend services.
Learn how to build production-ready REST APIs using Go's standard library and best practices.
A deep dive into Go's interface system and how it enables polymorphism and flexible code design.
An introduction to the Go programming language and why it's great for building web applications.
Where you draw service boundaries — and how you treat the contracts between them — decides whether a system is genuinely decoupled or a distributed monolith wearing microservice clothes. On data ownership, coupling, and evolving contracts without breaking consumers.
If you already know React, most of ClojureScript is closer than you think. Reagent is React with immutable data and hiccup instead of JSX — here's the mental map, the interop story, and why reference-equality re-rendering is the quiet win.
Redux (React/JS) and re-frame (ClojureScript) start from the same idea — one immutable app state, pure updates, derived views — and diverge in instructive ways. What each gets right, and what re-frame's effects and interceptors add.
The two problems every GraphQL server hits once real clients arrive: the N+1 query explosion from nested resolvers, and unbounded query cost. How to fix both in Go with dataloaders and complexity limits.
Three ways to expose an API, three different bets. A practical comparison of REST, gRPC, and GraphQL across contracts, transport, client fit, evolution, and caching — with the concrete conditions under which each is the right call.
What Datomic gives a data-heavy domain that a traditional database doesn't: the atomic datom, schema as data, immutable database values you query as pure functions, time-travel and audit for free—and an honest look at where it bites.
Cache-aside vs write-through, TTLs with jitter, and the cache stampede that takes your database down at the worst possible moment — with Go and Redis, and an honest look at the invalidation problem nobody escapes.
Making Clojure fast on large datasets: why lazy-seq pipelines allocate, how transducers and transients cut that to near zero, killing reflection in hot loops, and using core.async pipelines for I/O-bound fan-out with backpressure.
REPL-driven development is not 'a better console' — it's editing a live program. How I build functions incrementally against real data from inside my editor, using rich comment blocks and tap>, and where the workflow bites.
Building a backend-for-frontend in Clojure with Ring, Reitit, and malli: data-driven routing, typed HTTP contracts without a static type system, parallel fan-out to downstream services, and graceful degradation when one of them is slow.